Anomalous Sound Detection by Feature-Level Anomaly Simulation

Recently a growing number of works focus on machine defect detection from anomalous audio patterns. The datasets for the machine audio domain are scarce and recent methods that perform well on benchmarks such as DCASE2020 Task 2, rely on auxiliary information such as annotated data from other training classes in the domain to extract information that can be used in deep-learning classification-based anomaly detection approaches. However, in practical scenarios, annotated data from the same domain may not be readily available so annotation-free methods that can learn appropriate audio representations from unannotated data are needed. We propose AudDSR, a simulation-based anomaly detection method that learns to detect anomalies without additional annotated data and instead focuses on a discrete feature space sampling method for an anomaly simulation process. AudDSR outperforms competing methods that do not rely on annotated data on the DCASE2020 anomalous sound detection benchmark and even matches the performance of some methods that utilize additional annotation information.